Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review
Over the last few years, deep learning (DL) techniques have gained popularity and have
become the new standard for data processing in remote sensing analysis. Deep learning …
become the new standard for data processing in remote sensing analysis. Deep learning …
A synergistical attention model for semantic segmentation of remote sensing images
In remotely sensed images, high intraclass variance and interclass similarity are ubiquitous
due to complex scenes and objects with multivariate features, making semantic …
due to complex scenes and objects with multivariate features, making semantic …
LPMSNet: Location pooling multi-scale network for cloud and cloud shadow segmentation
X Dai, K Chen, M Xia, L Weng, H Lin - Remote Sensing, 2023 - mdpi.com
Among the most difficult difficulties in contemporary satellite image-processing subjects is
cloud and cloud shade segmentation. Due to substantial background noise interference …
cloud and cloud shade segmentation. Due to substantial background noise interference …
[HTML][HTML] Transformer-based decoder designs for semantic segmentation on remotely sensed images
Transformers have demonstrated remarkable accomplishments in several natural language
processing (NLP) tasks as well as image processing tasks. Herein, we present a deep …
processing (NLP) tasks as well as image processing tasks. Herein, we present a deep …
Detection of standing dead trees after pine wilt disease outbreak with airborne remote sensing imagery by multi-scale spatial attention deep learning and Gaussian …
Z Han, W Hu, S Peng, H Lin, J Zhang, J Zhou, P Wang… - Remote Sensing, 2022 - mdpi.com
The continuous and extensive pinewood nematode disease has seriously threatened the
sustainable development of forestry in China. At present, many studies have used high …
sustainable development of forestry in China. At present, many studies have used high …
Encoding contextual information by interlacing transformer and convolution for remote sensing imagery semantic segmentation
Contextual information plays a pivotal role in the semantic segmentation of remote sensing
imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The …
imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The …
Semantic segmentation of remote sensing images by interactive representation refinement and geometric prior-guided inference
High spatial resolution remote sensing images (HRRSIs) contain intricate details and varied
spectral distributions, making their semantic segmentation a challenging task. To address …
spectral distributions, making their semantic segmentation a challenging task. To address …
Semantic Segmentation of China's Coastal Wetlands Based on Sentinel-2 and Segformer
X Lin, Y Cheng, G Chen, W Chen, R Chen, D Gao… - Remote Sensing, 2023 - mdpi.com
Concerning the ever-changing wetland environment, the efficient extraction of wetland
information holds great significance for the research and management of wetland …
information holds great significance for the research and management of wetland …
SSCNet: A spectrum-space collaborative network for semantic segmentation of remote sensing images
Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing
images (RSIs). However, conventional methods predominantly focus on learning …
images (RSIs). However, conventional methods predominantly focus on learning …
A deformable attention network for high-resolution remote sensing images semantic segmentation
Deformable convolutional networks (DCNs) can mitigate the inherent limited geometric
transformation. We reformulate the spatialwise attention mechanism using DCNs in this …
transformation. We reformulate the spatialwise attention mechanism using DCNs in this …